Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus

Juan A. Gómez-Pulido, Enrique Cortés-Toro, Arturo Durán-Domínguez, Broderick Crawford, Ricardo Soto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The prediction of the students’ performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. This collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, considering matrix factorization and stochastic gradient descent. If we consider a fixed number of latent factors, the prediction error is mainly influenced by two parameters: learning rate and regularization factor. The best settings for these parameters is an optimization problem that can be tackled by soft computing techniques. In this work, we analyze three solving methods to select the optimal values of both parameters: a simple direct search, a classic evolutionary algorithm, and a novel metaheuristic. The results show the advantages of using metaheuristics instead of direct search in accuracy and computing effort terms.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
EditorsHujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
PublisherSpringer Verlag
Pages125-133
Number of pages9
ISBN (Print)9783030034924
DOIs
StatePublished - 2018
Externally publishedYes
Event19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain
Duration: 21 Nov 201823 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11314 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Country/TerritorySpain
CityMadrid
Period21/11/1823/11/18

Keywords

  • Genetic algorithms
  • Learning rate
  • Matrix factorization
  • Metaheuristics
  • Prediction
  • Recommender systems
  • Regularization factor
  • Stochastic gradient descent
  • Vapour-liquid equilibrium

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